How to integrate Scrapegraph ai MCP with Mastra AI

This guide walks you through connecting Scrapegraph ai to Mastra AI using the Composio tool router. By the end, you'll have a working Scrapegraph ai agent that can extract product prices from amazon search results, summarize latest news headlines from bbc homepage, convert wikipedia article to markdown format through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Scrapegraph ai account through Composio's Scrapegraph ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Scrapegraph ai

Api Key

Scrapegraph ai is an AI-powered web scraping API that lets you extract structured data from any website using natural language prompts. Easily turn web pages into ready-to-use data for your apps and agents.

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Introduction

This guide will help you understand how to give your Mastra AI agent real control over a Scrapegraph ai account through Composio's Scrapegraph ai MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Scrapegraph ai with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

Set up your environment so Mastra, OpenAI, and Composio work together
Create a Tool Router session in Composio that exposes Scrapegraph ai tools
Connect Mastra's MCP client to the Composio generated MCP URL
Fetch Scrapegraph ai tool definitions and attach them as a toolset
Build a Mastra agent that can reason, call tools, and return structured results
Run an interactive CLI where you can chat with your Scrapegraph ai agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

MCP Client: Built-in support for Model Context Protocol servers
Toolsets: Organize tools into logical groups
Step Callbacks: Monitor and debug agent execution
OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Scrapegraph ai MCP server, and what's possible with it?

The Scrapegraph ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrapegraph ai account. It provides structured and secure access to powerful web scraping and data extraction tools, so your agent can perform actions like running AI-powered scrapers, converting webpages to markdown, monitoring job statuses, and managing your account usage with ease.

AI-powered web scraping and search: Instruct your agent to extract structured data from any website or perform detailed web searches with parsed, organized results.
Webpage to markdown conversion: Let your agent instantly convert any webpage into clean, readable markdown for easy documentation or analysis.
Automated job status tracking: Check on the progress and results of ongoing scraping, crawling, or conversion jobs to stay updated without manual effort.
Smart multi-page crawling: Direct the agent to launch intelligent crawlers that gather data across multiple linked pages in a single workflow.
Account usage monitoring and feedback: Retrieve your remaining credits, track API usage, and submit feedback on completed tasks—all through your AI agent.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS

Prerequisites

Before starting, make sure you have:

Node.js 18 or higher
A Composio account with an active API key
An OpenAI API key
Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key.
You need credits or a connected billing setup to use the models.
Store the key somewhere safe.

Composio API Key

Log in to the Composio dashboard.
Go to Settings and copy your API key.
This key lets your Mastra agent talk to Composio and reach Scrapegraph ai through MCP.

Install dependencies

bash

npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

@composio/core is the Composio SDK for creating MCP sessions
@mastra/core provides the Agent class
@mastra/mcp is Mastra's MCP client
@ai-sdk/openai is the model wrapper for OpenAI
dotenv loads environment variables from .env

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your requests to Composio
COMPOSIO_USER_ID tells Composio which user this session belongs to
OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});

What's happening:

dotenv/config auto loads your .env so process.env.* is available
openai gives you a Mastra compatible model wrapper
Agent is the Mastra agent that will call tools and produce answers
MCPClient connects Mastra to your Composio MCP server
Composio is used to create a Tool Router session

Create a Tool Router session for Scrapegraph ai

typescript

async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["scrapegraph_ai"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Scrapegraph ai MCP URL:", composioMCPUrl);

What's happening:

create spins up a short-lived MCP HTTP endpoint for this user
The toolkits array contains "scrapegraph_ai" for Scrapegraph ai access
session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript

const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);

What's happening:

MCPClient takes an id for this client and a list of MCP servers
The headers property includes the x-api-key for authentication
getTools fetches the tool definitions exposed by the Scrapegraph ai toolkit

Create the Mastra agent

typescript

const agent = new Agent({
    name: "scrapegraph_ai-mastra-agent",
    instructions: "You are an AI agent with Scrapegraph ai tools via Composio.",
    model: "openai/gpt-5",
  });

What's happening:

Agent is the core Mastra agent
name is just an identifier for logging and debugging
instructions guide the agent to use tools instead of only answering in natural language
model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript

let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        scrapegraph_ai: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

What's happening:

messages keeps the full conversation history in Mastra's expected format
agent.generate runs the agent with conversation history and Scrapegraph ai toolsets
maxSteps limits how many tool calls the agent can take in a single run
onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Scrapegraph ai and Mastra AI:

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["scrapegraph_ai"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      scrapegraph_ai: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "scrapegraph_ai-mastra-agent",
    instructions: "You are an AI agent with Scrapegraph ai tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { scrapegraph_ai: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Scrapegraph ai through Composio's Tool Router. You can extend this further by:

Adding other toolkits like Gmail, Slack, or GitHub
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows

TOOLS

Supported Tools

Every Scrapegraph ai action and event your agent gets out of the box.

Convert Webpage to Markdown (V2)

Tool to convert any webpage into clean, well-formatted Markdown with full parameter control.

Generate Schema

Generate or modify a JSON schema based on a search query for structured data extraction.

Get Agentic Scraper History

Retrieve paginated history of agentic scraper jobs.

Get Crawler History

Retrieve the history of crawler jobs for your account.

Get Credits

Retrieve remaining and used credits for your ScrapeGraphAI account.

Get Endpoint Suggestions

Tool to get AI-powered suggestions for creating scraping endpoints.

Get Live Session URL

Tool to get a URL for a live browser session.

Get Markdownify History

Tool to retrieve the history of markdownify webpage-to-Markdown conversion jobs.

Get Scrape History

Retrieve the history of scrape jobs from your ScrapeGraphAI account.

Get Searchscraper History

Get the history of searchscraper jobs with pagination support.

Get Sitemap History

Tool to retrieve the history of sitemap extraction jobs.

Get Smartscraper History

Tool to retrieve the history of smartscraper jobs.

Get Usage Timeline

Tool to retrieve usage timeline statistics for your ScrapeGraphAI account.

Get Webhook Logs

Tool to retrieve webhook delivery logs for a crawler job.

List Scheduled Jobs

Retrieve a paginated list of all scheduled scraping jobs for your account.

Markdownify Status

Check the status and retrieve results of a Markdownify webpage-to-Markdown conversion job.

Save Endpoint Configuration

Tool to save custom scraping endpoint configurations to ScrapeGraphAI.

Search Scraper

Perform AI-powered web searches with structured, parsed results.

Check SearchScraper Status

Check the status and results of an asynchronous SearchScraper job.

SmartCrawler Status

Check the status and retrieve results of a SmartCrawler web crawling job.

Start Smart Scraper

Start AI-powered web scraping with natural language extraction prompts.

SmartScraper Status

Check the status and retrieve results of a SmartScraper web scraping job.

Start Smart Crawler (Async)

Tool to start a multi-page web crawl using SmartCrawler for AI-powered data extraction.

Submit Feedback

Submit feedback and ratings for completed ScrapeGraphAI requests.

Submit Product Feedback

Submit product feedback for ScrapeGraphAI.

Convert JSON to TOON Format

Tool to convert JSON data to TOON (Token-Oriented Object Notation) format.

Validate API Key

Validate your ScrapeGraphAI API key to ensure it is active and authorized.

FRAMEWORKS

How to build Scrapegraph ai MCP Agent with another framework

OpenAI Agents SDK

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Claude Agent SDK

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Claude Code

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Claude Cowork

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Codex

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OpenClaw

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Hermes

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CLI

Use Scrapegraph ai MCP with CLI

Google ADK

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LangChain

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Vercel AI SDK

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LlamaIndex

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CrewAI

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FAQ

Frequently asked questions

With a standalone Scrapegraph ai MCP server, the agents and LLMs can only access a fixed set of Scrapegraph ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Scrapegraph ai and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Scrapegraph ai tools.

Yes, absolutely. You can configure which Scrapegraph ai scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Scrapegraph ai data and credentials are handled as safely as possible.

Start with Scrapegraph ai.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Scrapegraph ai tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Scrapegraph ai MCP with Mastra AI

Connect Scrapegraph ai without auth hassles

Introduction

Also integrate Scrapegraph ai with

TL;DR

What is Mastra AI?

What is the Scrapegraph ai MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import libraries and validate environment

Create a Tool Router session for Scrapegraph ai

Configure Mastra MCP client and fetch tools

Create the Mastra agent

Set up interactive chat interface

Complete Code

Conclusion

Supported Tools

How to build Scrapegraph ai MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

LlamaIndex

CrewAI

Explore Other Toolkits

Excel

21risk

Abstract

Addressfinder

Frequently asked questions

What are the differences in Tool Router MCP and Scrapegraph ai MCP?+

Can I use Tool Router MCP with Mastra AI?+

Can I manage the permissions and scopes for Scrapegraph ai while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Scrapegraph ai.It takes 30 seconds.

Teams ship real agents on Composio